Benchmark of a large scale database for facial beauty prediction

Zhai, Yikui; Huang, Yu; Xu, Ying; Zeng, Junying; Yu, F. Richard; Gan, Junying

doi:10.1145/3028842.3028863

Cited by 10 publications

(12 citation statements)

References 21 publications

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“…Hence, Yan et al [18] proposed a cost-sensitive sequence regression (CSSR) FBP method, through extracting and testing the original pixels and texture features, successfully counter the problem of unbalanced data set categories, reaching a classification accuracy of 52.12%. Furthermore, an extensive database named large scale Asian female beauty database (LSAFBD) was established by Zhai et al [19] to settle the problem of insufficient facial data via applying apparent multiscale features to FBP. The above experiments reveal that if the texture feature uses the part of the whole face as the feature extraction object, it can substantially reduce manual intervention and improve model calculation efficiency.…”

Section: A Facial Beauty Predictionmentioning

confidence: 99%

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Zhai

Qin

et al. 2020

IEEE Access

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Facial beauty prediction (FBP), as a frontier topic in the domain of artificial intelligence regarding anthropology, has witnessed some good results as deep learning technology progressively develops. However, it is still limited by the complexity of the deep structure network in need of a large number of parameters and high dimensions, easily leading to a great consumption of time. To solve this problem, this paper proposes a fast training FBP method based on local feature fusion and broad learning system (BLS). Firstly, two-dimensional principal component analysis (2DPCA) is employed to reduce the dimension of the local texture image so as to lessen its redundancy. Secondly, local feature fusion method is adopted to extract more advanced features through avoiding the effects from unstable illumination, individual differences, and various postures. Finally, extensional feature eigenvectors are input to the broad learning network to train an efficient FBP model, which effectively shortens operational time and improve its preciseness. Extensive experiments with the proposed method on large scale Asian female beauty database (LSAFBD) can be conducted within 13.33s while sustaining an accuracy of 58.97%, impressively outstripping other state-of-the-art methods in training speed. INDEX TERMS Facial beauty prediction (FBP); local feature fusion; broad learning system (BLS);

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Section: A Facial Beauty Predictionmentioning

confidence: 99%

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Zhai

Qin

et al. 2020

IEEE Access

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“…However, it cannot achieve satisfactory results in unconstrained facial beauty prediction, while the landmark detection may be seriously affected by many factors, such as illumination, occlusion, and blurring. To avoid heavily manual intervention and burden landmark in geometry-based methods, and take advantage of large data, we established a large database named LSFBD in [15], and multiscale apparent features are utilized for facial beauty prediction. In this paper, we continue to explore the potential of CNN on the facial beauty prediction task based on the LSFBD.…”

Section: Related Workmentioning

confidence: 99%

“…LSFBD is a large-scale facial beauty database constructed by Zhai et al [15], which is used in facial beauty prediction as a benchmark. LSFBD contains 20,000 labeled images, including 10,000 unconstrained male images and 10,000 unconstrained female images.…”

Section: Experiments and Analysismentioning

confidence: 99%

BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

Zhai

Cao

Deng

et al. 2019

Computational Intelligence and Neuroscience

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Because of the lack of discriminative face representations and scarcity of labeled training data, facial beauty prediction (FBP), which aims at assessing facial attractiveness automatically, has become a challenging pattern recognition problem. Inspired by recent promising work on fine-grained image classification using the multiscale architecture to extend the diversity of deep features, BeautyNet for unconstrained facial beauty prediction is proposed in this paper. Firstly, a multiscale network is adopted to improve the discriminative of face features. Secondly, to alleviate the computational burden of the multiscale architecture, MFM (max-feature-map) is utilized as an activation function which can not only lighten the network and speed network convergence but also benefit the performance. Finally, transfer learning strategy is introduced here to mitigate the overfitting phenomenon which is caused by the scarcity of labeled facial beauty samples and improves the proposed BeautyNet’s performance. Extensive experiments performed on LSFBD demonstrate that the proposed scheme outperforms the state-of-the-art methods, which can achieve 67.48% classification accuracy.

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“…In recent years, most studies of FBP are based on deep learning [5]- [7]. Although these methods have achieved good results, there are still some challenges.…”

Section: Introductionmentioning

confidence: 99%

“…SCUT-FBP5500 [6] is a facial beauty database of 5500 images, constructed by South China University of Technology. Our group has built a Large Scale Facial Beauty Database (LSFBD) [7], including 20,000 labeled images (10000 male images and 10000 female images) and 80000 unlabeled images. However, training a deep convolutional neural network by a small database is prone to overfitting.…”

Section: Introductionmentioning

confidence: 99%

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

Gan

Scotti

et al. 2020

IEEE Access

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Facial beauty prediction (FBP) has become an emerging area in the field of artificial intelligence. However, the lacks of data and accurate face representation hinder the development of FBP. Multi-task transfer learning can effectively avoid over-fitting, and utilize auxiliary information of related tasks to optimize the main task. In this paper, we present a network named Multi-input Multi-task Beauty Network (2M BeautyNet) and use transfer learning to predict facial beauty. In the experiment, beauty prediction is the main task, and gender recognition is the auxiliary. For multi-task training, we employ multitask loss weights automatic learning strategy to improve the performance of FBP. Finally, we replace the softmax classifier with a random forest. We conduct experiments on the Large Scale Facial Beauty Database (LSFBD) and SCUT-FBP5500 database. Results show that our method has achieved good results on LSFBD, the accuracy of FBP is up to 68.23%. Our 2M BeautyNet structure is suitable for multiple inputs of different databases. INDEX TERMS Facial beauty prediction, multi-task transfer learning, multi-input multi-output network, multi-task loss weight automatic learning strategy.

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Benchmark of a large scale database for facial beauty prediction

Cited by 10 publications

References 21 publications

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

Facial Beauty Prediction via Local Feature Fusion and Broad Learning System

BeautyNet: Joint Multiscale CNN and Transfer Learning Method for Unconstrained Facial Beauty Prediction

2M BeautyNet: Facial Beauty Prediction Based on Multi-Task Transfer Learning

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